Исследование влияния пола и психологических характеристик автора на количественные параметры его текста с использованием программы Linguistic Inquiry and Word Count

Book review: Applied cryptography: Protocols, algorithms, and source code in C

Recognition of sign languages has gained reasonable interest by the researchers in the last decade. An accurate sign language recognition system can facilitate more accurate communication of deaf and dumb people. The wide variety of Indian Sign Language (ISL) led to more challenging learning process. In the current work, three novel methods was reported to solve the problem of recognition of ISL gestures effectively by combining Neural Network (NN) with Genetic Algorithm (GA), Evolutionary algorithm (EA) and Particle Swarm Optimization (PSO) separately to attain novel NN-GA, NN-EA and NN-PSO methods; respectively. The input weight vector to the NN has been optimized gradually to achieve minimum error. The proposed methods performance was compared to NN and the Multilayer Perceptron Feed-Forward Network (MLP-FFN) classifiers. Several performance metrics such as the accuracy, precision, recall, F-measure and kappa statistic were calculated. The experimental results established that the proposed algorithm achieved considerable improvement over the performance of existing works in order to recognize ISL gestures. The NN-PSO outperformed the other approaches with 99.96 accuracy, 99.98 precision, 98.29 recall, 99.63 F-Measure and 0.9956 Kappa Statistic.

Indian Sign Language Recognition Using Optimized Neural Networks

Electroencephalographic (EEG)recordings are affected data-segment, butitleads toaconsiderable infor- employed inordertoinvestigate thebrainactivity inneu- mation loss. A verypowerful approach, butstill notcommon ropathological subjects. Unfortunately EEG areoften contam-inclinical practice, wasproposed inliterature someyears inated bytheartifacts, signals thathavenon-cerebral origin iccticaroposinlitratu omeyears andthatmight mimiccognitive or pathologic activity and agic onsinartact signals extraction, detection therefore distort theanalysis ofEEG.Inthis paper weproposeandcancellation. Thisapproach concentrates theartifactual amultiresolution analysis, based onEEGwavelet processing, to content oftheEEGdataset inafewsignals toberejected, so extract thecerebral EEG rhythms. We alsopresent amethod that wedonothavetocancel theentire affected datasegment basedonRenyi's entropy andkurtosis toautomatically identify(1),(2),(3),(4). theWavelet components affected byartifacts. Finally, wediscuss asthejoint useofwavelet analysis, kurtosis andRenyi's entropy Obviously, artifact rejection always involves a lossof allows foradeeper investigation ofthebrainactivity andwe information, eventhough small, anditcauses alittle BEG discuss thecapability ofthis technique tobecomeanefficient distortion. Inaddition, manyartifacts havefixed characteris- preprocessing step tooptimize artifact rejection fromEEG.This tics inthefrequency domainandtheydistort theEEGonly isthefirst technique thatexploits thepeculiarities ofEEG to inaspecific frequency range. Optimize EEG artifact detection. i pcfcfeunyrne

Brain Activity Investigation byEEGProcessing: Wavelet Analysis, Kurtosis andRenyi's Entropy forArtifact Detection

Sign Language is a natural language which deaf community uses for communication. Sign Language (SL) is a subset of gestures or signs made with fingers, hands, arms, eyes, and head, face etc. Each gesture in SL has a meaning assigned to it. Understanding SL is nothing but understanding the meaning of these gestures. There exists a problem in communication when a person who completely relies on this gestural SL for communication tries to converse with a person who does not understand the SL. Every country has its own developed SL. In India, this language is called as "Indian Sign Language (ISL). This paper aims to develop an algorithm that will translate the ISL into English. This paper has implemented a system named as "Indian Sign Language (ISL) Translator using Gesture recognition algorithm". The system translates gestures made in ISL into English. The gestures that have been translated include numbers, alphabets and few phrases. The algorithm first performs data acquisition, then the pre-processing of gestures is performed to track hand movement using a combinational algorithm, and recognition is done using template matching. The database used for implementation has been self-created and includes total 130,000 videos; out of which 72,000 videos were used to create the system database and remaining 58,000 videos have been tested for checking the performance of the system. The accuracy of this system is as high as 97.5%.

Indian sign language translator using gesture recognition algorithm

Understanding human motions can be posed as a pattern recognition problem. Applications of pattern recognition in information processing problems are diverse ranging from Speech, Handwritten character recognition to medical research and astronomy. Humans express time-varying motion patterns (gestures), such as a wave, in order to convey a message to a recipient. If a computer can detect and distinguish these human motion patterns, the desired message can be reconstructed, and the computer can respond appropriately. This paper represents a framework for a human computer interface capable of recognizing gestures from the Indian sign language. The complexity of Indian sign language recognition system increases due to the involvement of both the hands and also the overlapping of the hands. Alphabets and numbers have been recognized successfully. This system can be extended for words and sentences Recognition is done with PCA (Principal Component analysis). This paper also proposes recognition with neural networks. Further it is proposed that number of finger tips and the distance of fingertips from the centroid of the hand can be used along with PCA for robustness and efficient results.

Indian sign language recognition

Automatic and tunable algorithm for EEG artifact removal using wavelet decomposition with applications in predictive modeling during auditory tasks

Security of data is very important in data communication. Everyday a lot of information is transferred form one user to another on internet and so the possibility of data theft also increases. Steganography provides a solution for the security of information during data transmission. Steganography is the science which makes the valuable information invisible to prevent it from unauthorized user. In this paper an audio message has been embedded in an image using the LSB (Least Significant Bit) technique and the wavelet transform. To hide a speech in an image is challenging as size of speech is larger than size of image. Number of bits in 1kb of speech is almost equal to an image. This paper describes how maximum speech can be embed in an image.

/pdf/audio-in-image-steganography-based-on-wavelet-transform-205pfj73tt.pdf

Audio in Image Steganography based on Wavelet Transform

This chapter introduces the applications of wavelet for Electroencephalogram (EEG) signal analysis. First, the overview of EEG signal is discussed to the recording of raw EEG and widely used frequency bands in EEG studies. The chapter then progresses to discuss the common artefacts that contaminate EEG signal while recording. With a short overview of wavelet analysis techniques, namely; Continues Wavelet Transform (CWT), Discrete Wavelet Transform (DWT), and Wavelet Packet Decomposition (WPD), the chapter demonstrates the richness of CWT over conventional time-frequency analysis technique e.g. Short-Time Fourier Transform. Lastly, artefact removal algorithms based on Independent Component Analysis (ICA) and wavelet are discussed and a comparative analysis is demonstrated. The techniques covered in this chapter show that wavelet analysis is well-suited for EEG signals for describing time-localised event. Due to similar nature, wavelet analysis is also suitable for other biomedical signals such as Electrocardiogram and Electromyogram.

/pdf/wavelets-for-eeg-analysis-1jkdtqk37u.pdf

Wavelets for EEG Analysis

Fractals have always been associated with the term chaos. To many chaologists, the study of chaos and fractals is more than just a new field in science which fascinates the researchers to work on a theory that unifies mathematics, physics, art, and computer science - it is a revolution in science. Nature has used fractal designs for at least hundreds of millions of years and here we are focusing on the work done on fractal art in fashion design. This review represents a survey of the significant advances on fractal art in fashion design, both practical and theoretical, since the early publication of 1918. Results of our initial work in same direction are also shown in paper. This art can be used to generate unimaginable patterns which can increase the creativity of designers and give promising results to the fashion industry.

Nikesh Bajaj

Papers

Indian sign language recognition

Automatic and tunable algorithm for EEG artifact removal using wavelet decomposition with applications in predictive modeling during auditory tasks

Audio in Image Steganography based on Wavelet Transform

Wavelets for EEG Analysis

An exploration of fractal art in fashion design